H3CSA: A makespan aware task scheduling technique for cloud environments

2021 ◽  
Author(s):  
Ashutosh Mishra ◽  
Manmath Narayan Sahoo ◽  
Anurag Satpathy
Keyword(s):  
Task Scheduling ◽  
Cloud Environments ◽  
Scheduling Technique

scholarly journals Evolutionary algorithm-based multi-objective task scheduling optimization model in cloud environments

2015 ◽  
Vol 18 (6) ◽  
pp. 1737-1757 ◽  
Author(s):  
Fahimeh Ramezani ◽  
Jie Lu ◽  
Javid Taheri ◽  
Farookh Khadeer Hussain
Keyword(s):  
Evolutionary Algorithm ◽  
Task Scheduling ◽  
Optimization Model ◽  
Scheduling Optimization ◽  
Multi Objective ◽  
Cloud Environments

scholarly journals Teaching-Learning based Task Scheduling Optimization in Cloud Computing Environments

2019 ◽  
Vol 8 (2) ◽  
pp. 2952-2958
Keyword(s):  
Task Scheduling ◽  
Research Work ◽  
Search Space ◽  
Scheduling Problem ◽  
Optimal Resource ◽  
Np Hard Problem ◽  
Cloud Environments ◽  
Computing Environments ◽  
Teaching Learning ◽  
First Time

Generating optimal task scheduling plans in cloud environments is a tedious task as it is a np-hard problem. The optimal resource allocation in cloud environments involves more search space and time consuming. Therefore, recent researchers are focused on implementation of artificial intelligence to solve task scheduling problem. In this paper, a new and efficient evolutionary algorithm named teaching-learning based algorithm has been implemented first time to solve the task scheduling problem in cloud environments. The current research work considers the task scheduling problem as a multi-objective optimization problem. The proposed algorithm finds the best solution by minimizing the execution time and response time while maximizing the throughput of all resources to complete the assigned tasks.

A Task Scheduling Approach for Cloud Environments Employing Evolutionary Algorithms

2021 ◽  
Vol 13 (2) ◽  
pp. 423-438
Author(s):  
B. Lakhani ◽  
A. Agrawal
Keyword(s):  
Evolutionary Algorithms ◽  
Response Time ◽  
Task Scheduling ◽  
Gradient Descent ◽  
Virtual Machines ◽  
Sufficient Information ◽  
Swarm Optimization ◽  
Cloud Environments ◽  
Time Critical ◽  
Cloud Resources

One of the key challenges in the domain of cloud computing is task scheduling and estimation of cloud workloads for time critical applications pertaining to constrained cloud resources. While effective task scheduling is necessary for balancing the load, workload forecasting is necessary to plan in advance the requirements of cloud platforms based on previous data so as to effectively utilize cloud resources. Often it is challenging to gather sufficient information about the tasks and hence allocating the tasks to virtual machines (VMs) in the most optimal way is non-trivial. In this paper, a hybrid task scheduling approach is proposed based on evolutionary algorithms. The first approach is the amalgamation of bat and particle swarm optimization (PSO) techniques. The scheduling approach also combines the processing time preemption (PTP) approach to schedule the source intensive tasks which allows to reduce the response time of the proposed system.  The second approach is a machine learning based approach employing gradient descent with momentum (GDM). The evaluation of the proposed system has been done based on the response time and mean square error of the system.

scholarly journals An IoT-based task scheduling optimization scheme considering the deadline and cost-aware scientific workflow for cloud computing

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Xiaojin Ma ◽  
Honghao Gao ◽  
Huahu Xu ◽  
Minjie Bian
Keyword(s):  
Task Scheduling ◽  
Large Scale ◽  
Scheduling Algorithm ◽  
Scientific Workflow ◽  
Scheduling Method ◽  
Cloud Environments ◽  
Deadline Constraints ◽  
Minimum Completion Time ◽  
Acquisition Delay ◽  
Dynamic Acquisition

Abstract Large-scale applications of Internet of things (IoT), which require considerable computing tasks and storage resources, are increasingly deployed in cloud environments. Compared with the traditional computing model, characteristics of the cloud such as pay-as-you-go, unlimited expansion, and dynamic acquisition represent different conveniences for these applications using the IoT architecture. One of the major challenges is to satisfy the quality of service requirements while assigning resources to tasks. In this paper, we propose a deadline and cost-aware scheduling algorithm that minimizes the execution cost of a workflow under deadline constraints in the infrastructure as a service (IaaS) model. Considering the virtual machine (VM) performance variation and acquisition delay, we first divide tasks into different levels according to the topological structure so that no dependency exists between tasks at the same level. Three strings are used to code the genes in the proposed algorithm to better reflect the heterogeneous and resilient characteristics of cloud environments. Then, HEFT is used to generate individuals with the minimum completion time and cost. Novel schemes are developed for crossover and mutation to increase the diversity of the solutions. Based on this process, a task scheduling method that considers cost and deadlines is proposed. Experiments on workflows that simulate the structured tasks of the IoT demonstrate that our algorithm achieves a high success rate and performs well compared to state-of-the-art algorithms.

Task Scheduling in Cloud Environments

2020 ◽  
pp. 213-255
Author(s):  
Fahimeh Ramezani ◽  
Mohsen Naderpour ◽  
Javid Taheri ◽  
Jack Romanous ◽  
Albert Y. Zomaya
Keyword(s):  
Task Scheduling ◽  
Cloud Environments
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